A review on automatic identification of fovea in retinal fundus images

Rajesh, I.S.; Arikerie, Bharathi Malakreddy; Reshmi, Bharati M.

doi:10.1504/ijmei.2020.106900

Cited by 1 publication

(1 citation statement)

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“…The morphological characteristics of the macula (e.g. circularity, intensity, absence of vessels, position relative to the retina center, distance from the OD and relative position to vascular structure) have been explored by macula localization methods [17], [18]. In the work proposed by AKRAM et al [19], intensity is used to identify dark regions from the retinal texture.…”

Section: Related Work: Synthesis Of Macular Location Methodsmentioning

confidence: 99%

A Robustness Study of Machine Learning Based Methods for Macula Detection in Pathological Fundus Images

Elloumi,

Kachouri

2023

2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA)

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The macula detection is an important step for screening retinal diseases. Several methods have been proposed for detecting macula, with the aim of achieving a higher detection performance despite retinal pathologies. Their processing principles are based on well-known anatomical criteria of macula. However, these criteria are subject to significant changes caused by various eye pathologies. Therefore, it leads to a non-performing macula detection, and hence pathology screening. The objective of this work is to study the robustness of the macula detection methods in terms of retinal pathologies. For this purpose, the main macular criteria are extracted. Then, recent macula detection methods are detailed where the macula criteria taken into account are identified. Thereafter, the retinal pathologies that affect the macula are listed where caused macula changes are figured out. This study allows us to synthesise the robustness of each method in terms of pathologies.

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Section: Related Work: Synthesis Of Macular Location Methodsmentioning

confidence: 99%